Bulletin of the American Physical Society
2023 APS March Meeting
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session Y70: Quantum System Learning
8:00 AM–11:00 AM,
Friday, March 10, 2023
Room: Room 409
Sponsoring
Unit:
DQI
Chair: Shouvanik Chakrabarti, JPMorgan Chase
Abstract: Y70.00012 : Decoding surface codes with deep reinforcement learning and probabilistic policy reuse*
10:12 AM–10:24 AM
Presenter:
Elisha Siddiqui Matekole
(Riverlane)
Authors:
Elisha Siddiqui Matekole
(Riverlane)
Esther Ye
(Department of Physics, Boston University, Boston, MA 02215, USA)
Ramya Iyer
(Stanford University, Stanford, CA 94305, USA)
Samuel Yen-Chi Chen
(Brookhaven National Laboratory)
Tzu-Chieh Wei
(Stony Brook University)
A significant amount of theoretical studies have provided various types of QEC codes; one of the notable topological codes is the surface code. Recent developments of ML-based techniques especially the reinforcement learning (RL) methods are trying to tackle this challenge and have already made certain progress. Nevertheless, the device noise pattern may change over time, making trained decoder models ineffective. In this work, we propose a continual reinforcement learning method to tackle these decoding challenges. Specifically, we construct a double deep Q-learning with probabilistic policy reuse (DDQN-PPR) model to learn surface code decoding strategies of quantum environments with varying noise patterns.
*This work is supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics program under Award Number DE-SC-0012704, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internships Program (SULI) & BNL High School Research Program (HSRP) and the Brookhaven National Laboratory LDRD #20-024. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAPm4138.
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